class_docstr: Preserves the original input data without any transformations.
  
  Due to the restrictions of torch.Tensor, the encoding function only accepts types:
  
  - (nested) lists of numbers
  - np.ndarray of numbers, or
  - torch.Tensor
  
  Nonetypes are automatically converted into nans. This cannot be decoded.
  If self.handle_nan is True, then all nans are converted to zeros and not decoded back.
  
  The decode function takes in a torch.Tensor and converts it to a list of numbers.
docstr: Nothing to prepare 
docstr_tok: ["Nothing","to","prepare"]
code: def prepare(self, priming_data: Iterable[Union[float, int]]) -> None:
      """Nothing to prepare"""
      self._prepared = True
  
file_path: lightwood/lightwood/encoder/identity/identity.py
imports: import numpy
  from typing import List, Iterable, Union
  import torch
  from lightwood.encoder.base import BaseEncoder
label_desc: Use image node_id 2 for calling the IdentityEncoder obj's underlying member method code with example usage: obj.prepare(priming_data) without return types